In recent years, vehicle control systems, like those used in aircraft, automotive, and marine vehicles, have grown progressively more complex with the proliferation of newer and more powerful controllers. The vehicle control systems include one or more controllers capable of implementing a greater number of complex algorithms for measuring and/or controlling different aspects of a vehicle. Control systems continue to incorporate applications of advanced control theory that previously were not feasible to implement due to SW/HW limitations. In addition, the applications of the control theory continue to be modified for adaptation on newer processor architectures.
Vehicle control systems include implementations of control theory based on whether a state of the vehicle is known. In some examples, the state of the vehicle is known (measured), which enables the vehicle control system to generate a control command based on a known state of the vehicle. However, in some instances, the state of the vehicle is partially known or completely unknown. In this case, only output measurements from the vehicle sensors (such as IMU and rate gyros) are available to synthesize a control policy. An unknown state of the vehicle presents additional challenges to the vehicle control system such as, for example, generating a control command based on an estimation of the state of the vehicle. The estimation of the state of the vehicle requires additional processing power to perform complex algorithmic calculations based on advanced control theory techniques. Algorithms that rely on estimation of the vehicle state based on a suite of sensors are called “the observer-based control”.